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通过基于注意力的堆叠自动编码器增强网络安全,并使用优化算法来检测和减轻对物联网辅助网络的攻击.

Kashi Sai Prasad1, E Laxmi Lydia2, M V Rajesh3

  • 1Department of CSE-AI&ML, MLR Institute of Technology, Hyderabad, India.

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概括

本研究介绍了物联网 (IoT) 网络的新网络安全方法. 该CASAE-POADMA方法有效地检测和减轻网络攻击,准确率为99.50%.

关键词:
网络安全攻击的攻击.深度学习是一种深度学习.功能选择 功能选择物联网的物联网,就是物联网.佩利坎优化算法的优化算法

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科学领域:

  • 网络安全 网络安全
  • 网络安全 网络安全
  • 在物联网中的机器学习

背景情况:

  • 物联网 (IoT) 网络面临越来越多的网络安全威胁,原因是大规模的连接.
  • 传统的入侵检测系统 (IDS) 难以应对物联网设备的规模和多样性.
  • 机器学习 (ML) 和深度学习 (DL) 为物联网安全挑战提供了有前途的解决方案.

研究的目的:

  • 提出一种新的方法,CASAE-POADMA,用于检测和减轻物联网网络中的网络安全攻击.
  • 通过先进的计算技术,提高物联网辅助环境的安全态度.
  • 在不断变化的物联网威胁的背景下,解决现有安全措施的局限性.

主要方法:

  • 在CASAE-POADMA方法论中,用于数据缩放的min-max规范化.
  • 功能选择是使用灰色优化 (GGO) 方法进行的.
  • 通过基于注意力的堆叠自动编码器 (ASAE) 来实现网络安全攻击的检测和缓解,并通过子优化算法 (POA) 进行超参数调整.

主要成果:

  • 该CASAE-POADMA方法在识别和减轻网络安全攻击方面表现出卓越的表现.
  • 在基准数据库上的实验验证产生了99.50%的高精度.
  • 拟议的方法在保护物联网网络方面显著优于现有的技术.

结论:

  • CASAE-POADMA方法提供了一种有效的解决方案,用于增强物联网网络中的网络安全.
  • 基于注意力的堆叠自动编码器和鱼优化算法的集成为攻击检测和缓解提供了一个强大的框架.
  • 实现的高精度验证了这种方法在现实世界物联网安全应用中的潜力.